Here, \(\tau _{out}\)  is timeout of local update dumping in the PoW process, \(\mathcal {T}_g\)  is the block arrival/generation delay, \(p_{fork}\)  is the forking probability, and \(\tau _{global}\)  and \(\tau _{gdn}\)  are the delays for global model update and global model downloading, respectively. The latency is then optimized by an online delay minimization algorithm with respect to transmission frame size, block size, and block arrival rate under different channel conditions. Simulation results reveal the benefits of the joint consideration of communication latency and consensus delay in achieving an optimal FLchain training latency, and the positive impacts of blockchain parameters, e.g., block arrival rate, on the overall model learning performance. However, the comparison of delay performances of the proposed FLchain and advanced FL schemes in edge computing [1]} has not been implemented.
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